AI Visibility Showdown: Which Car Brands Dominate AI Search in the USA?

We started by analyzing how U.S. consumers search for cars using traditional SEO tools and auto-market reports. We identified thousands of keyword patterns tied to specific buying goals.

Then, instead of using these keywords directly, we translated them into natural-language prompts—the kind of questions people ask AI assistants.

To reflect actual consumer use cases, we grouped prompts into six car-buying intent categories—based on how people think about cars when searching:

• Electric Cars (sustainability, tech-forward)
• Family Cars (space, safety, practicality)
• Affordable Cars (entry-level, budget-conscious)
• Large SUVs (space, off-road, towing)
• Luxury Cars (performance, prestige, comfort)
• Safe Cars (crash-test scores, features)

Each group contained 20 unique prompts, resulting in 60 prompt-model combinations across the three AI search engines.

We recorded how often each brand appeared in AI-generated responses across all prompts. If a brand was explicitly recommended or discussed, it counted as a mention.

We then applied weightings to reflect relative usage and trust levels of each search engine:

• GPT 4o Search: 100
• Gemini 2.5 Flash: 50
• Perplexity: 25

This produced a weighted visibility score for each brand, adjusted for market impact.

In AI search, visibility depends not on keyword density, but on how well your brand appears in answers to natural questions.

A keyword like "safe midsize SUV" is no longer enough.

A model might see the prompt "Which midsize SUVs have the highest crash safety ratings?" and return only a few handpicked brands—leaving others invisible.

In AI results, users don't see 10 links—they see 1–2 paragraphs of synthesized insight.

That means AI engines are choosing which brands to include, often mentioning only 2–3 models per prompt.

Your brand either appears in the short answer—or it doesn't.

Visibility is highly sensitive to how the question is phrased. If your product is known for safety but the prompt emphasizes affordability, you might be excluded.

Even the distribution of prompt attributes matters: If 50% of prompts use the word "electric" but only 15% of buyers care about EVs, your visibility score might be artificially skewed toward brands dominating that segment.

AI models often struggle with different ways of writing brand names.

A prompt using "Mercedes" might yield different results than "Mercedes-Benz."

Smaller or newer brands may get overlooked entirely if they lack sufficient digital context.

Without proper normalization, even good brands can be undercounted.

We simulate real-world buyer intent by crafting hundreds of prompts using actual consumer search behavior patterns.

Each prompt is designed to reflect how a real person would naturally ask a question—ensuring realistic, accurate data.

We use browser automation and API integrations to query GPT 4o Search, Gemini 2.5 Flash, and Perplexity with consistent inputs.

We then parse and structure AI-generated responses to extract brand mentions, citations, and position in the answer.

We apply dynamic weights to reflect current popularity of each AI engine—so our results reflect how users actually distribute their attention across platforms.

To avoid skewed visibility, we test different prompt variations and compare the stability of brand appearances.

This helps us adjust for prompt bias and highlight reliable patterns, not one-off anomalies.

Some AI engines (e.g. Perplexity) show citations or sources.

We track which websites, product pages, or review platforms are contributing to your brand's mentions—so you know not just if you're visible, but why.

Our custom dashboards show changes in AI visibility over time, segmented by intent category and engine.

You can see if your brand is growing in visibility for "family cars" or losing ground in "luxury segment" month-over-month.